AI-Enhanced EHR System with Intelligent Prescription Processing and Automated Patient Engagement

Year : 2026 | Volume : 15 | Issue : 02 | Page : 28 34
By

Jayesh Digambar Deore,

Kunal Deelip Bagad,

Ashwin Sharad Benke,

Vedant Dilip Gadade,

Ms. P. J. Patel,

  1. Student, Department of Artificial Intelligence and Data Science, Jawahar Education Society’s Institute of Technology, Management and Research Centre, Nashik, Maharashtra, India
  2. Student, Department of Artificial Intelligence and Data Science, Jawahar Education Society’s Institute of Technology, Management and Research Centre, Nashik, Maharashtra, India
  3. Student, Department of Artificial Intelligence and Data Science, Jawahar Education Society’s Institute of Technology, Management and Research Centre, Nashik, Maharashtra, India
  4. Assistant Professor, Department of Artificial Intelligence and Data Science, Jawahar Education Society’s Institute of Technology, Management and Research Centre, Nashik, Maharashtra, India
  5. Assistant Professor, Department of Artificial Intelligence and Data Science, Jawahar Education Society’s Institute of Technology, Management and Research Centre, Nashik, Maharashtra, India

Abstract

AI-Enhanced EHR System with Intelligent Prescription Processing and Automated Patient Engagement: A Comprehensive Electronic Health Record and Hospital Management System Healthcare institutions increasingly require digital solutions to manage the growing complexity of patient data, staff coordination, and multi-branch operations. This paper presents an AI-Enhanced EHR System with Intelligent Prescription Processing and Automated Patient Engagement, a full-stack Hospital Management System (HMS) designed to streamline clinical and administrative workflows within modern healthcare environments. The system was developed using Next.js 15 with Tailwind CSS for the frontend and Django REST Framework (DRF) with SimpleJWT for the backend, with SQLite for development and PostgreSQL support for production deployments. The architecture follows a RESTful API design, enabling a clean separation between the client and server layers. The system provides a centralized platform supporting multi-hospital branch management, patient record creation and retrieval, employee and role management, and secure JWT-based authentication. The interactive dashboard offers real-time operational statistics, while user-facing features, such as dark mode, profile settings, and notification preferences, enhance usability. The system was evaluated for its ability to handle core hospital management tasks efficiently, demonstrating reliable data flow between the frontend and backend components. The modular architecture ensures scalability and ease of future extension. This solution addresses key challenges in healthcare digitization, particularly in resource-constrained environments, by offering an open-source, maintainable, and extensible platform. Future work includes integration of appointment scheduling, billing management, and AI-assisted diagnostics support.

Keywords: Electronic health record, hospital management system, intelligent prescription processing, automated patient engagement, Django rest framework

[This article belongs to Research and Reviews : A Journal of Medical Science and Technology ]

How to cite this article: Jayesh Digambar Deore, Kunal Deelip Bagad, Ashwin Sharad Benke, Vedant Dilip Gadade, Ms. P. J. Patel. AI-Enhanced EHR System with Intelligent Prescription Processing and Automated Patient Engagement. Research and Reviews : A Journal of Medical Science and Technology. 2026; 15(02):28-34.
How to cite this URL: Jayesh Digambar Deore, Kunal Deelip Bagad, Ashwin Sharad Benke, Vedant Dilip Gadade, Ms. P. J. Patel. AI-Enhanced EHR System with Intelligent Prescription Processing and Automated Patient Engagement. Research and Reviews : A Journal of Medical Science and Technology. 2026; 15(02):28-34. Available from: https://journals.stmjournals.com/rrjomst/article=2026/view=253500

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Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 08/05/2026
Accepted 26/06/2026
Published 27/08/2026
Publication Time 111 Days

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